Evidence map›Paper›PMID 42124272›Full record

ArticleMaterials (Basel, Switzerland)2026

Stress-Driven Generation of Continuous Fibrous Material Paths for Additive Manufacturing: Numerical Assessment and Manufacturing Feasibility.

Andrea Sellitto, Aniello Riccio

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Andrea SellittoDepartment of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, CE, Italy.ORCID 0000-0001-7766-8250
Aniello RiccioDepartment of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, CE, Italy.ORCID 0000-0001-7426-6803

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work presents a methodology for the generation of continuous fibre trajectories based on principal stress directions in continuous fibre-reinforced additive manufacturing (CFAM). The material system considered consists of continuous carbon fibre (CCF-1.5K) embedded in a CFC-PA thermoplastic matrix. CFAM enables the deposition of fibres along tailored paths, allowing improved alignment with the load direction, compared to traditional composite manufacturing. In this way, the strong anisotropy of composite materials, typically considered a limitation, is exploited as a design opportunity by aligning fibres with the structural load paths. The proposed approach combines finite element analysis with a path generation procedure, including the computation of principal stress directions, the extraction of streamlines of the principal stress field, and a dedicated post-processing stage aimed at obtaining continuous and manufacturable fibre layouts. The effectiveness of the method is assessed through a finite element-based comparison with conventional fibre configurations, showing an increase in global stiffness of approximately 20% with respect to the best-performing unidirectional layout. In addition, the feasibility of the generated trajectories is demonstrated through printing tests performed on a continuous fibre additive manufacturing system. The results confirm that the proposed methodology enables the generation of physically realizable fibre paths while improving structural performance.

Indexed as

anisotropycomposite materialscontinuous fibre additive manufacturingfibre orientationfinite element analysisprincipal stress directionsstreamline-based path planningtoolpath generation

Identifiers

PMID42124272
PMCPMC13165037

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.